{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-01-14T11:21:53.635843Z",
     "start_time": "2025-01-14T11:21:53.553402Z"
    }
   },
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "from sklearn.cluster import DBSCAN\n",
    "from sklearn.datasets import make_blobs\n",
    "from sklearn.ensemble import IsolationForest"
   ],
   "outputs": [],
   "execution_count": 20
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 1.基于分位数找异常值 | Numeric Outlier\n",
   "id": "fa88233a224bd0bf"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-14T10:11:32.001496Z",
     "start_time": "2025-01-14T10:11:31.992500Z"
    }
   },
   "cell_type": "code",
   "source": [
    "np.random.seed(42)  # 设置随机种子\n",
    "x = np.random.rand(50, 1)  # 生成50个随机数，格式为二维数据\n",
    "print(x)\n",
    "print(\"-\" * 50)\n",
    "\n",
    "x[0, 0] = 3\n",
    "x[1, 0] = 2\n",
    "\n",
    "print(x)"
   ],
   "id": "c8b88763d68455d0",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.37454012]\n",
      " [0.95071431]\n",
      " [0.73199394]\n",
      " [0.59865848]\n",
      " [0.15601864]\n",
      " [0.15599452]\n",
      " [0.05808361]\n",
      " [0.86617615]\n",
      " [0.60111501]\n",
      " [0.70807258]\n",
      " [0.02058449]\n",
      " [0.96990985]\n",
      " [0.83244264]\n",
      " [0.21233911]\n",
      " [0.18182497]\n",
      " [0.18340451]\n",
      " [0.30424224]\n",
      " [0.52475643]\n",
      " [0.43194502]\n",
      " [0.29122914]\n",
      " [0.61185289]\n",
      " [0.13949386]\n",
      " [0.29214465]\n",
      " [0.36636184]\n",
      " [0.45606998]\n",
      " [0.78517596]\n",
      " [0.19967378]\n",
      " [0.51423444]\n",
      " [0.59241457]\n",
      " [0.04645041]\n",
      " [0.60754485]\n",
      " [0.17052412]\n",
      " [0.06505159]\n",
      " [0.94888554]\n",
      " [0.96563203]\n",
      " [0.80839735]\n",
      " [0.30461377]\n",
      " [0.09767211]\n",
      " [0.68423303]\n",
      " [0.44015249]\n",
      " [0.12203823]\n",
      " [0.49517691]\n",
      " [0.03438852]\n",
      " [0.9093204 ]\n",
      " [0.25877998]\n",
      " [0.66252228]\n",
      " [0.31171108]\n",
      " [0.52006802]\n",
      " [0.54671028]\n",
      " [0.18485446]]\n",
      "--------------------------------------------------\n",
      "[[3.        ]\n",
      " [2.        ]\n",
      " [0.73199394]\n",
      " [0.59865848]\n",
      " [0.15601864]\n",
      " [0.15599452]\n",
      " [0.05808361]\n",
      " [0.86617615]\n",
      " [0.60111501]\n",
      " [0.70807258]\n",
      " [0.02058449]\n",
      " [0.96990985]\n",
      " [0.83244264]\n",
      " [0.21233911]\n",
      " [0.18182497]\n",
      " [0.18340451]\n",
      " [0.30424224]\n",
      " [0.52475643]\n",
      " [0.43194502]\n",
      " [0.29122914]\n",
      " [0.61185289]\n",
      " [0.13949386]\n",
      " [0.29214465]\n",
      " [0.36636184]\n",
      " [0.45606998]\n",
      " [0.78517596]\n",
      " [0.19967378]\n",
      " [0.51423444]\n",
      " [0.59241457]\n",
      " [0.04645041]\n",
      " [0.60754485]\n",
      " [0.17052412]\n",
      " [0.06505159]\n",
      " [0.94888554]\n",
      " [0.96563203]\n",
      " [0.80839735]\n",
      " [0.30461377]\n",
      " [0.09767211]\n",
      " [0.68423303]\n",
      " [0.44015249]\n",
      " [0.12203823]\n",
      " [0.49517691]\n",
      " [0.03438852]\n",
      " [0.9093204 ]\n",
      " [0.25877998]\n",
      " [0.66252228]\n",
      " [0.31171108]\n",
      " [0.52006802]\n",
      " [0.54671028]\n",
      " [0.18485446]]\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-14T10:15:41.759127Z",
     "start_time": "2025-01-14T10:15:41.739836Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 假设df是一个Pandas DataFrame，包含了你想要分析的数据集\n",
    "# 'column_name'是你想要检测异常值的列名\n",
    "\n",
    "df = pd.DataFrame(x, columns=['column_name'])\n",
    "\n",
    "# 设置分位数，例如四分位数\n",
    "Q1 = df[\"column_name\"].quantile(0.25)  # 第一四分位数（25%）\n",
    "Q3 = df[\"column_name\"].quantile(0.75)  # 第三四分位数（75%）\n",
    "# 计算四分位数范围（IQR）\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "# 定义下界和上界来识别异常值\n",
    "lower_bound = Q1 - 1.5 * IQR  # 下界\n",
    "upper_bound = Q3 + 1.5 * IQR  # 上界\n",
    "\n",
    "# 标记异常值\n",
    "df[\"outliers\"] = df[\"column_name\"].apply((lambda x: \"YES\" if x < lower_bound or x > upper_bound else \"NO\"))\n",
    "\n",
    "# 打印出被标记为异常值的行\n",
    "df[df[\"outliers\"] == \"YES\"]"
   ],
   "id": "1f5ae2e0af434c94",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   column_name outliers\n",
       "0          3.0      YES\n",
       "1          2.0      YES"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>column_name</th>\n",
       "      <th>outliers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 2.基于Z-score找异常值 | Z-score Outlier",
   "id": "52cb6dd72bf918e8"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "# 如果Z-score为正，表示该数值高于均值；如果Z-score为负，表示该数值低于均值；如果Z-score为0，表示该数值等于均值\n",
    "# 异常值检测：通常，Z-score绝对值大于3的数据点被认为是异常值。"
   ],
   "id": "a98a6d88c0802ec4"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 3.基于DBSCAN找异常值 | DBSCAN Outlier",
   "id": "791e4bb18afc7241"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-14T11:01:47.447436Z",
     "start_time": "2025-01-14T11:01:47.300946Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# DBSCAN是一种密度聚类算法，它将数据点分为核心点、边界点和噪声点。\n",
    "# 它通过计算每个数据点周围的密度来确定核心点，并将邻近的核心点连接成簇。\n",
    "# 边界点是邻近核心点数量不足以形成簇的数据点，而噪声点则是孤立的数据点。\n",
    "# 该算法的优点是可以发现任意形状的簇，并且对于噪声点具有鲁棒性。\n",
    "\n",
    "# 生成样本数据\n",
    "n_samples = 1500\n",
    "random_state = 170\n",
    "# make_blobs生成两个簇的样本数据\n",
    "x, y = make_blobs(n_samples=n_samples, random_state=random_state)  # y没有用\n",
    "\n",
    "print(x.shape)\n",
    "\n",
    "# 增加一些噪声点，往X中拼接噪声点\n",
    "rng = np.random.RandomState(74)\n",
    "# concaenate()函数用于拼接两个数组\n",
    "# uniform()函数用于生成均匀分布的随机数\n",
    "x = np.concatenate((x, rng.uniform(low=-10, high=10, size=(100, 2))))  # 100个噪声点\n",
    "\n",
    "print(x.shape)  # #1600个样本，2列特征，100个噪声点\n",
    "\n",
    "# 可视化生成的数据\n",
    "# scatter()函数用于绘制散点图\n",
    "plt.scatter(x[:, 0], x[:, 1], s=5)\n",
    "plt.title(\"Generated Data with Noise\")\n",
    "plt.show()\n"
   ],
   "id": "61860b5fb82d36f0",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1500, 2)\n",
      "(1600, 2)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-14T11:12:46.768093Z",
     "start_time": "2025-01-14T11:12:46.621535Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# DBSCAN算法中的两个重要参数是半径(epsilon)和最小样本数(min_samples)，它们可以用来定义核心点。\n",
    "# 对于给定点p，计算在以其为圆心，以半径epsilon为半径的圆内包含的点的数量，记为N(p)\n",
    "# 判断核心点：如果点p的密度大于等于min_samples，则将其标记为核心点。\n",
    "# 判断边界点：如果点p的密度小于min_samples但是在以其为圆心，以半径epsilon为半径的圆内包含了至少一个核心点，则将其标记为边界点。\n",
    "# 判断噪声点：如果点p既不是核心点也不是边界点，则将其标记为噪声点。\n",
    "\n",
    "# 使用DBSCAN算法检测异常值\n",
    "# eps: 半径参数，即核心点的半径\n",
    "# min_samples: 最小样本数，即核心点的数量\n",
    "db = DBSCAN(eps=0.5, min_samples=5).fit(x)\n",
    "\n",
    "labels = db.labels_  # 获取每个样本的标签\n",
    "print(np.unique(labels))  # 输出标签的种类 [-1  0  1  2]\n",
    "\n",
    "# 标签为-1的点是噪声点\n",
    "noise_mask = (labels == -1)\n",
    "\n",
    "print(np.sum(noise_mask)) # 输出噪声点的数量 131\n",
    "print(np.unique(labels[~noise_mask]))  # 得到噪声点的mask\n",
    "\n",
    "# 可视化聚类结果\n",
    "# c参数用于指定颜色，cmap参数用于指定颜色映射,s参数用于指定点的大小\n",
    "# x[~noise_mask, 0] : 取出非噪声点的第一列特征，即横坐标\n",
    "# x[~noise_mask, 1] : 取出非噪声点的第二列特征，即纵坐标\n",
    "plt.scatter(x[~noise_mask, 0],x[~noise_mask, 1],c=labels[~noise_mask],s=5,cmap=\"viridis\") # 正常点用彩色标注\n",
    "\n",
    "plt.scatter(x[noise_mask, 0],x[noise_mask, 1],c=\"red\",s=5) # 噪声点用红色标注\n",
    "\n",
    "# legend()函数用于添加图例\n",
    "plt.legend([\"Cluster 0\", \"Cluster 1\", \"Cluster 2\", \"Noise\"])\n",
    "plt.show()"
   ],
   "id": "378123aa04f55d62",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1  0  1  2]\n",
      "131\n",
      "[0 1 2]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 18
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 4.孤立森林",
   "id": "76ecc7cd51dd245b"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-14T11:28:03.277886Z",
     "start_time": "2025-01-14T11:28:03.042555Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 孤立森林的计算过程包括以下几个步骤：\n",
    "# 构建随机树：从数据集中随机选择一个属性(特征）和一个随机分割点，将数据集一分为二，并递归地对子节点进行同样的操作，直到每个叶节点只包含一个数据点或者达到最大深度。\n",
    "# 重复步骤1多次，构建多棵随机树（n个特征就有n颗树）。\n",
    "# 计算异常分数：对于每个数据点，在多棵树上计算其平均深度，并将深度转换为异常分数。异常分数越低，则该数据点越可能是异常点。\n",
    "# 对异常分数进行阈值判断，将小于阈值的数据点标记为异常点。\n",
    "# 需要注意的是，在计算异常分数时，可以采用路径长度或者路径长度的指数来作为深度的度量指标，具体实现方式可以根据具体情况而定\n",
    "\n",
    "# 因为孤立森林是通过随机分割数据构建树来识别异常点的，异常点相对于正常点来说更容易被随机分割到单独的叶节点上，也就是更浅的深度上，因此异常点的平均深度会比正常点更小，对应的异常分数则会更低。\n",
    "\n",
    "np.random.seed(42) # 设置随机种子\n",
    "x=np.random.rand(50,2) # 生成50个随机数，格式为二维数据,好画图来解释\n",
    "\n",
    "# IsolationForest算法\n",
    "# contamination参数表示异常点的比例，默认值为0.1，即异常点占总样本的10%\n",
    "iso_forest=IsolationForest(random_state=42,contamination=0.1)\n",
    "\n",
    "# 训练模型，随机建树的过程\n",
    "iso_forest.fit(x) \n",
    "\n",
    "# 预测每个点的异常分数，在树中的高度越低，越可能是异常点\n",
    "scores=iso_forest.decision_function(x) # 计算异常分数\n",
    "\n",
    "# 比较发现：5个最小分数的点被看作是异常点，其余的点被看作是正常点\n",
    "\n",
    "print(scores)\n",
    "print(\"-\" * 50)\n",
    "\n",
    "# 预测每个点的标签，异常点的标签为-1，正常点的标签为1\n",
    "labels=iso_forest.predict(x) \n",
    "print(labels)\n",
    "print(\"-\" * 50)\n",
    "\n",
    "# 绘制数据点和异常点\n",
    "# c=labels 用于指定颜色，cmap=plt.cm.bwr 用于指定颜色映射\n",
    "# edgecolors=\"k\" 用于指定边框颜色\n",
    "# s=50 用于指定点的大小\n",
    "plt.scatter(x[:,0],x[:,1],c=labels,cmap=plt.cm.bwr,edgecolors=\"k\",s=50) \n",
    "plt.title('Isolation Forest Anomaly Detection') \n",
    "plt.xlabel('Feature 1')\n",
    "plt.ylabel('Feature 2')\n",
    "plt.show()\n",
    "\n"
   ],
   "id": "e330e51db37c44b4",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.00105308  0.10055646  0.05361504  0.05024246  0.07184847  0.00404901\n",
      "  0.01997561  0.0627385   0.10884232  0.0783621   0.0611147   0.08402063\n",
      "  0.04548452  0.07878843  0.00254178  0.08008609  0.0508405  -0.01082588\n",
      "  0.06452119  0.0800867   0.04634851  0.04934762  0.06599273  0.10829868\n",
      "  0.07446152 -0.01120788 -0.05423651  0.00130106  0.02542394  0.00763327\n",
      "  0.07608262  0.04074583  0.09872945  0.05188861 -0.00947773  0.0588507\n",
      "  0.01964547  0.0595183   0.00608521  0.06445908  0.03844004  0.03177732\n",
      "  0.09384891  0.09728622  0.01367941  0.04172716  0.09544099  0.09193243\n",
      "  0.06956676 -0.02362362]\n",
      "--------------------------------------------------\n",
      "[ 1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1 -1  1  1  1  1  1  1\n",
      "  1 -1 -1  1  1  1  1  1  1  1 -1  1  1  1  1  1  1  1  1  1  1  1  1  1\n",
      "  1 -1]\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 23
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-14T11:31:14.652911Z",
     "start_time": "2025-01-14T11:31:14.639099Z"
    }
   },
   "cell_type": "code",
   "source": [
    "df=pd.DataFrame(np.c_[x,scores,labels],columns=['Feature 1','Feature 2','scores','Label'])\n",
    "df"
   ],
   "id": "47f780aad9e1f152",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "    Feature 1  Feature 2    scores  Label\n",
       "0    0.374540   0.950714  0.001053    1.0\n",
       "1    0.731994   0.598658  0.100556    1.0\n",
       "2    0.156019   0.155995  0.053615    1.0\n",
       "3    0.058084   0.866176  0.050242    1.0\n",
       "4    0.601115   0.708073  0.071848    1.0\n",
       "5    0.020584   0.969910  0.004049    1.0\n",
       "6    0.832443   0.212339  0.019976    1.0\n",
       "7    0.181825   0.183405  0.062738    1.0\n",
       "8    0.304242   0.524756  0.108842    1.0\n",
       "9    0.431945   0.291229  0.078362    1.0\n",
       "10   0.611853   0.139494  0.061115    1.0\n",
       "11   0.292145   0.366362  0.084021    1.0\n",
       "12   0.456070   0.785176  0.045485    1.0\n",
       "13   0.199674   0.514234  0.078788    1.0\n",
       "14   0.592415   0.046450  0.002542    1.0\n",
       "15   0.607545   0.170524  0.080086    1.0\n",
       "16   0.065052   0.948886  0.050840    1.0\n",
       "17   0.965632   0.808397 -0.010826   -1.0\n",
       "18   0.304614   0.097672  0.064521    1.0\n",
       "19   0.684233   0.440152  0.080087    1.0\n",
       "20   0.122038   0.495177  0.046349    1.0\n",
       "21   0.034389   0.909320  0.049348    1.0\n",
       "22   0.258780   0.662522  0.065993    1.0\n",
       "23   0.311711   0.520068  0.108299    1.0\n",
       "24   0.546710   0.184854  0.074462    1.0\n",
       "25   0.969585   0.775133 -0.011208   -1.0\n",
       "26   0.939499   0.894827 -0.054237   -1.0\n",
       "27   0.597900   0.921874  0.001301    1.0\n",
       "28   0.088493   0.195983  0.025424    1.0\n",
       "29   0.045227   0.325330  0.007633    1.0\n",
       "30   0.388677   0.271349  0.076083    1.0\n",
       "31   0.828738   0.356753  0.040746    1.0\n",
       "32   0.280935   0.542696  0.098729    1.0\n",
       "33   0.140924   0.802197  0.051889    1.0\n",
       "34   0.074551   0.986887 -0.009478   -1.0\n",
       "35   0.772245   0.198716  0.058851    1.0\n",
       "36   0.005522   0.815461  0.019645    1.0\n",
       "37   0.706857   0.729007  0.059518    1.0\n",
       "38   0.771270   0.074045  0.006085    1.0\n",
       "39   0.358466   0.115869  0.064459    1.0\n",
       "40   0.863103   0.623298  0.038440    1.0\n",
       "41   0.330898   0.063558  0.031777    1.0\n",
       "42   0.310982   0.325183  0.093849    1.0\n",
       "43   0.729606   0.637557  0.097286    1.0\n",
       "44   0.887213   0.472215  0.013679    1.0\n",
       "45   0.119594   0.713245  0.041727    1.0\n",
       "46   0.760785   0.561277  0.095441    1.0\n",
       "47   0.770967   0.493796  0.091932    1.0\n",
       "48   0.522733   0.427541  0.069567    1.0\n",
       "49   0.025419   0.107891 -0.023624   -1.0"
      ],
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature 1</th>\n",
       "      <th>Feature 2</th>\n",
       "      <th>scores</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.374540</td>\n",
       "      <td>0.950714</td>\n",
       "      <td>0.001053</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.731994</td>\n",
       "      <td>0.598658</td>\n",
       "      <td>0.100556</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.156019</td>\n",
       "      <td>0.155995</td>\n",
       "      <td>0.053615</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.058084</td>\n",
       "      <td>0.866176</td>\n",
       "      <td>0.050242</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.601115</td>\n",
       "      <td>0.708073</td>\n",
       "      <td>0.071848</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.020584</td>\n",
       "      <td>0.969910</td>\n",
       "      <td>0.004049</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.832443</td>\n",
       "      <td>0.212339</td>\n",
       "      <td>0.019976</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.181825</td>\n",
       "      <td>0.183405</td>\n",
       "      <td>0.062738</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.304242</td>\n",
       "      <td>0.524756</td>\n",
       "      <td>0.108842</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.431945</td>\n",
       "      <td>0.291229</td>\n",
       "      <td>0.078362</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.611853</td>\n",
       "      <td>0.139494</td>\n",
       "      <td>0.061115</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.292145</td>\n",
       "      <td>0.366362</td>\n",
       "      <td>0.084021</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.456070</td>\n",
       "      <td>0.785176</td>\n",
       "      <td>0.045485</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.199674</td>\n",
       "      <td>0.514234</td>\n",
       "      <td>0.078788</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.592415</td>\n",
       "      <td>0.046450</td>\n",
       "      <td>0.002542</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.607545</td>\n",
       "      <td>0.170524</td>\n",
       "      <td>0.080086</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.065052</td>\n",
       "      <td>0.948886</td>\n",
       "      <td>0.050840</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.965632</td>\n",
       "      <td>0.808397</td>\n",
       "      <td>-0.010826</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.304614</td>\n",
       "      <td>0.097672</td>\n",
       "      <td>0.064521</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.684233</td>\n",
       "      <td>0.440152</td>\n",
       "      <td>0.080087</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.122038</td>\n",
       "      <td>0.495177</td>\n",
       "      <td>0.046349</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.034389</td>\n",
       "      <td>0.909320</td>\n",
       "      <td>0.049348</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.258780</td>\n",
       "      <td>0.662522</td>\n",
       "      <td>0.065993</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.311711</td>\n",
       "      <td>0.520068</td>\n",
       "      <td>0.108299</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.546710</td>\n",
       "      <td>0.184854</td>\n",
       "      <td>0.074462</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.969585</td>\n",
       "      <td>0.775133</td>\n",
       "      <td>-0.011208</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.939499</td>\n",
       "      <td>0.894827</td>\n",
       "      <td>-0.054237</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.597900</td>\n",
       "      <td>0.921874</td>\n",
       "      <td>0.001301</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.088493</td>\n",
       "      <td>0.195983</td>\n",
       "      <td>0.025424</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.045227</td>\n",
       "      <td>0.325330</td>\n",
       "      <td>0.007633</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.388677</td>\n",
       "      <td>0.271349</td>\n",
       "      <td>0.076083</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.828738</td>\n",
       "      <td>0.356753</td>\n",
       "      <td>0.040746</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.280935</td>\n",
       "      <td>0.542696</td>\n",
       "      <td>0.098729</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.140924</td>\n",
       "      <td>0.802197</td>\n",
       "      <td>0.051889</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.074551</td>\n",
       "      <td>0.986887</td>\n",
       "      <td>-0.009478</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.772245</td>\n",
       "      <td>0.198716</td>\n",
       "      <td>0.058851</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.005522</td>\n",
       "      <td>0.815461</td>\n",
       "      <td>0.019645</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.706857</td>\n",
       "      <td>0.729007</td>\n",
       "      <td>0.059518</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.771270</td>\n",
       "      <td>0.074045</td>\n",
       "      <td>0.006085</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.358466</td>\n",
       "      <td>0.115869</td>\n",
       "      <td>0.064459</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.863103</td>\n",
       "      <td>0.623298</td>\n",
       "      <td>0.038440</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.330898</td>\n",
       "      <td>0.063558</td>\n",
       "      <td>0.031777</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.310982</td>\n",
       "      <td>0.325183</td>\n",
       "      <td>0.093849</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.729606</td>\n",
       "      <td>0.637557</td>\n",
       "      <td>0.097286</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.887213</td>\n",
       "      <td>0.472215</td>\n",
       "      <td>0.013679</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.119594</td>\n",
       "      <td>0.713245</td>\n",
       "      <td>0.041727</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.760785</td>\n",
       "      <td>0.561277</td>\n",
       "      <td>0.095441</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.770967</td>\n",
       "      <td>0.493796</td>\n",
       "      <td>0.091932</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.522733</td>\n",
       "      <td>0.427541</td>\n",
       "      <td>0.069567</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.025419</td>\n",
       "      <td>0.107891</td>\n",
       "      <td>-0.023624</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 24,
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   "execution_count": 24
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   "cell_type": "code",
   "source": "labels[labels==-1]",
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